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Related Concept Videos

Quality of Water01:19

Quality of Water

509
In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
509
Quality Control01:05

Quality Control

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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
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Quality Assurance01:19

Quality Assurance

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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
1.0K
Pulse amplitude and quality01:17

Pulse amplitude and quality

3.0K
Pulse amplitude is a crucial indicator of cardiac health because it provides valuable insights into the strength of left ventricular contractions and the overall uniformity of blood circulation within the vasculature. The strength of the pulse is directly related to the force with which the heart contracts and the volume of blood being pumped.
A weak or absent pulse may indicate reduced cardiac output or poor left ventricular contraction, which can be signs of cardiovascular dysfunction or...
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Testing Water Quality01:14

Testing Water Quality

378
When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
378
Protein Folding Quality Check in the RER01:29

Protein Folding Quality Check in the RER

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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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A Framework for Visualizing Data Quality for Predictive Models and Clinical Quality Measures.

Steven G Johnson1, Lisiane Pruinelli1,2, Alexander Hoff3

  • 1Institute for Health Informatics, University of Minnesota.

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Summary
This summary is machine-generated.

An automated Healthcare Data Quality Framework (HDQF) efficiently assesses electronic health record (EHR) data quality for secondary use. This tool quickly identifies and visualizes data issues, saving time compared to manual methods.

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Area of Science:

  • Health Informatics
  • Data Science
  • Healthcare Analytics

Background:

  • Secondary use of Electronic Health Record (EHR) data is crucial for healthcare advancements.
  • Assessing and ensuring EHR data quality is a prerequisite for reliable secondary data utilization.
  • Manual data quality assessments are time-consuming and resource-intensive.

Purpose of the Study:

  • To introduce an automated Healthcare Data Quality Framework (HDQF) for assessing EHR data quality.
  • To demonstrate the utility of HDQF in identifying and visualizing data quality issues.
  • To evaluate the efficiency of HDQF for healthcare analytics use cases.

Main Methods:

  • Development and implementation of a general-purpose automated Healthcare Data Quality Framework (HDQF).
  • Application of HDQF to assess data quality for predictive modeling and clinical quality measure analytics.
  • Visualization of data quality assessment results to pinpoint areas for improvement.

Main Results:

  • Automated HDQF efficiently identifies and visualizes data quality issues in EHR datasets.
  • The framework significantly reduces the time required for data quality assessment compared to manual approaches.
  • HDQF can be repeatedly applied to new datasets with minimal additional effort.

Conclusions:

  • An automated HDQF is an effective tool for supporting healthcare organizations' data quality initiatives.
  • HDQF facilitates the secondary use of EHR data by ensuring data integrity and quality.
  • The framework offers a scalable and efficient solution for ongoing data quality management in healthcare.